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Human Posture Estimation: A Systematic Review on Force-Based Methods—Analyzing the Differences in Required Expertise and Result Benefits for Their Utilization

Helmstetter, Sebastian 1; Matthiesen, Sven 1
1 Institut für Produktentwicklung (IPEK), Karlsruher Institut für Technologie (KIT)

Abstract:

Force-based human posture estimation (FPE) provides a valuable alternative when camera-based human motion capturing is impractical. It offers new opportunities for sensor integration in smart products for patient monitoring, ergonomic optimization and sports science. Due to the interdisciplinary research on the topic, an overview of existing methods and the required expertise for their utilization is lacking. This paper presents a systematic review by the PRISMA 2020 review process. In total, 82 studies are selected (59 machine learning (ML)-based and 23 digital human model (DHM)-based posture estimation methods). The ML-based methods use input data from hardware sensors—mostly pressure mapping sensors—and trained ML models for estimating human posture. The ML-based human posture estimation algorithms mostly reach an accuracy above 90%. DHMs, which represent the structure and kinematics of the human body, adjust posture to minimize physical stress. The required expert knowledge for the utilization of these methods and their resulting benefits are analyzed and discussed. DHM-based methods have shown their general applicability without the need for application-specific training but require expertise in human physiology. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000165513
Veröffentlicht am 11.12.2023
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Produktentwicklung (IPEK)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2023
Sprache Englisch
Identifikator ISSN: 1424-8220
KITopen-ID: 1000165513
Erschienen in Sensors
Verlag MDPI
Band 23
Heft 21
Seiten Art.Nr.: 8997
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 06.11.2023
Schlagwörter human pose prediction; activity recognition; motion capture; classification; machine learning; digital human model; virtual sensor; biomechanics; pressure sensor
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